Resource Provisioning for Heterogeneous Services in UAV-Assisted MEC Systems
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is regarded as an important component in beyond fifth generation (B5G) communication network. However, sharing the UAV resources among heterogeneous services with different requirements would lead to conflicts. This paper considers a UAV-assisted MEC system to meet the low average service delay requirement of enhanced mobile broadband user equipments (eUEs) as well as fulfill the massive machine-type communication user equipments (mUEs) accessing simultaneously. In order to satisfy the differential requirements, we propose the resource provisioning scheme based on resource slicing to isolate the spectrum resource, which can effectively alleviate conflicts caused by heterogeneous services sharing homogeneity resource. Specifically, we formulate an optimization problem to minimize the average service delay of eUEs while maximizing the number of served mUEs by jointly optimizing the user equipments association, spectrum resource slicing, transmit power, and computation resource allocation. By leveraging the unique problem structure, a heterogeneous services-oriented resource provisioning algorithm is designed based on the coalition game and successive convex approximation. Simulation results validate that the proposed algorithm can achieve efficient resource provisioning to concurrently satisfy the requirements of heterogeneous services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".